* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download - Wiley Online Library
Climate governance wikipedia , lookup
Climate change denial wikipedia , lookup
Economics of global warming wikipedia , lookup
Pleistocene Park wikipedia , lookup
Global warming controversy wikipedia , lookup
Climate change adaptation wikipedia , lookup
Fred Singer wikipedia , lookup
General circulation model wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Solar radiation management wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Global warming wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Politics of global warming wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climate change in the United States wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Physical impacts of climate change wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Climate change feedback wikipedia , lookup
Effects of global warming wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Hotspot Ecosystem Research and Man's Impact On European Seas wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change, industry and society wikipedia , lookup
ORIGINAL RESEARCH Disentangling the signal of climatic fluctuations from land use: changes in ecosystem functioning in South American protected areas (1982-2012) n Dieguez1 & Jose M. Paruelo1,2 Herna lisis Regional y Teledeteccio n and Depto. de M n, IFEVA (CONICET- Facultad de Laboratorio de Ana etodos Cuantitativos y Sistemas de Informacio Agronomıa), Universidad de Buenos Aires, Av. San Martın 4453, Buenos Aires, C1417DSE, Argentina 2 blica, Buenos Aires, Uruguay Instituto de Ecologıa y Ciencias Ambientales, Facultad de Ciencias, Universidad de la Repu 1 Keywords Climate change, GIMMS, long term trends, NDVI, seasonality, sensitivity Correspondence Hern an Dieguez, Laboratorio de Analisis n and Depto. de Regional y Teledeteccio M etodos Cuantitativos y Sistemas de n, IFEVA (CONICET- Facultad de Informacio Agronomıa), Av. San Martın 4453, Buenos Aires, C1417DSE, Argentina. Tel: +54 11 4524-8073; Fax: +54 11 45248000; E-mail: [email protected] Funding Information This work was carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI) CRN3095 which is supported by the US National Science Foundation (Grant GEO-1128040). CONICET and UBA provided additional funds. Editor: Nathalie Pettorelli Associate Editor: Alienor Chauvenet Received: 29 July 2016; Revised: 11 January 2017; Accepted: 16 January 2017 Abstract Global environmental change is characterized by changing climate, atmospheric composition and land use. Its impact on ecosystem structure and functioning has been detected throughout the world. While every ecosystem is vulnerable to climate change, the degree of the impact and the magnitude of the ecosystem response are likely to vary. Protected areas of South America provide a ‘laboratory’ to test expectations of climate change effects on ecosystems at a regional scale. By using protected areas we minimized the effects of land use/land cover changes over ecosystem functioning. We analyzed the temporal trends, that is, directional changes, and spatial heterogeneity of both climatic variables and attributes of the seasonal dynamics of the normalized difference vegetation index, that is, a surrogate of vegetation carbon gains derived from satellite information, on 201 protected areas of South America. Increased productivity and higher seasonality, frequently climate driven, is the most common signal across South American biomes but concentrated on those areas located in the tropics and subtropics. In general, arid and semiarid sites responded positively to increases in precipitation and negatively to increases in temperature, while humid ecosystems responded in the opposite way. Our results provide a preliminary basis for predicting which ecosystems will respond more rapidly and strongly to climate change. We also provide support to the fact that protected areas are not static systems as their functioning is changing with different magnitude and in contrasting directions. doi: 10.1002/rse2.39 Introduction Global environmental change encompasses different interacting dimensions that alter the structure and function of Earth ecosystems (Vitousek 1994). The evidence about climate change, that is, global increases in temperature and changes in rainfall patterns, is vast and widely accepted (Huntington 2006; IPCC 2007, Mann et al. 2008). Carbon dioxide (CO2) concentration increase in the atmosphere as a consequence of human activities is the best documented component of Global Change (Vitousek 1994; Cook et al. 2016). Land use/cover change is modifying the Earth’s surface at unprecedented rates through afforestation and deforestation, agricultural expansion, intensification of livestock activities and urbanization (DeFries et al. 2004; Foley et al. 2005; Hansen et al. 2013). ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. 1 Ecosystem functioning in South American Protected Areas The capacity of the biosphere to provide ecosystem services in the long term is threatened by Global Change (Vitousek et al. 1997; Sala et al. 2000). Multiple alterations of ecosystem structure and functioning as a consequence of Global Change were detected in different areas of the planet through remote sensing, dendroecology and other sources of information, such as forest inventories (Spiecker 1999; Paruelo et al. 2004; Boisvenue and Running 2006). Behind those alterations diverse direct factors had been identified. Such factors were generally associated to three dimensions of Global Change: modification in climate, atmospheric composition and land use/cover. Protected Areas (PAs) are the cornerstone of in-situ global conservation efforts and are directed not only to preserve biodiversity but also to ensure the provision of multiple ecosystem services, including cultural services (Lopoukhine et al. 2012; Dudley et al. 2014; Watson et al. 2014). Protected Areas are not ecological islands or static systems; they are part of a broader socio-ecological context (Cumming et al. 2015) and are, in turn, affected by environmental changes (Alcaraz-Segura et al. 2008; Pettorelli et al. 2012). In South America, 20.4% of land surface is under protection, more than in any other continent (UN 2011). Despite the importance of PAs, many South American ecosystems are highly threatened (Myers et al. 2000) and are currently undergoing rapid transformations since some of its biomes present the highest deforestation rates in the world (Hansen et al. 2013). Besides, in South America there is a high disparity between habitat loss and protection (Hoekstra et al. 2005) and even inside protected areas land degradation is extended and increasing (Leisher et al. 2013). Despite this critical situation, our understanding of South American PAs ecosystem functioning is poor. A functional characterization of PAs can be used to derive a baseline or reference situation corresponding to the ‘potential’ functioning of ecosystems (Garbulsky and Paruelo 2004; Cabello et al. 2012). Having reference situations could also allow to disentangle the relative effects of some of the dimensions of Global Change, for example, land use/ cover change and climate/atmospheric changes. Ecosystem functioning analysis based on remote sensing techniques is recognized as a useful approach for studying Global Change (Kerr and Ostrovsky 2003; Pettorelli et al. 2005; Cabello et al. 2012). Many ecosystem functional studies are based on monitoring the temporal dynamics of the normalized difference vegetation index (NDVI), a spectral index associated with the fraction of the photosynthetic active radiation intercepted by green tissues (fPAR; Potter et al. 1993; Sellers et al. 1996; Di Bella et al. 2004), which in turn, is one of the main controls of Carbon (C) gains or net primary productivity 2 H. Dieguez & J.M. Paruelo (ANPP; Monteith 1972). Numerous studies have linked satellite derived NDVI with ANPP of different regions and ecosystems of the world, finding a strong correlation between spectral behavior and vegetation functioning (Ruimy et al. 1994; Paruelo et al. 1997; Xiao and Moody 2004; Pi~ neiro et al. 2006). Net primary productivity is the main input of C and energy into the ecosystem (Odum 1969) and it was proposed as an integrative variable of ecosystem functioning (McNaughton et al. 1989) and a descriptor of ecosystem health (Costanza 1992; Schlesinger 1997). Given its relationship with NPP, the NDVI magnitude and stability can be used as a surrogate of the provision of regulation ecosystem services (Paruelo et al. 2016). Furthermore, the analysis of NDVI dynamics and its attributes has been widely used to characterize the impact of land-use change impacts on ecosystem functioning, particularly on NPP (Hicke et al. 2002; Guerschman et al. 2003; Paruelo et al. 2004) and recently, on ecosystem services provision as well (Barral and Maceira 2012; Carre~ no et al. 2012; Volante et al. 2012; Paruelo et al. 2016). Several studies based on remote sensing techniques partially investigated Global Change effects on South American ecosystems. Garbulsky and Paruelo (2004) derived empirical relationships between ecosystem functional attributes and their variability across environmental gradients analyzing 13 PAs in Argentina. Alcaraz-Segura et al. (2013) explored the environmental and human controls of ecosystem functional diversity in temperate South America. Considering the temporal dimension, Paruelo et al. (2004) analyzed the trends of radiation interception during the period 1981–2000 in South America. Their results showed how land use/cover change controlled those trends in grasslands and dry forests of southern South America. Texeira et al. (2015) analyzed the control exerted by land cover and precipitation over long-term trends in ANPP in South American temperate grasslands and Hilker et al. (2014) in Amazon forests. Leisher et al. (2013) described land degradation across Latin American PAs and their surroundings in the period 2004–2009. Furthermore, land use/cover change impact on functional attributes of ecosystems were explored in southern South America (Guerschman et al. 2003; Volante et al. 2012; Vassallo et al. 2013; Texeira et al. 2015). So far, the climatic controls of C gain trends in South American ecosystems were investigated as part of global studies (Schultz and Halpert 1993; Ichii et al. 2002; Nemani et al. 2003; Seddon et al. 2016). However, the significant climatic changes observed in recent decades (Skansi et al. 2013) and expected for the future (IPCC 2007) highlight the need for a more comprehensive analysis in South American ecosystems disentangling the signal of climatic fluctuations from land use. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Water availability and temperature are the main determinants of NPP spatial variability (Lieth 1973). But whether or not interannual variability in climate account for interannual variability in NPP is both supported (e.g. Lauenroth and Sala 1992; Sala et al. 2012) and challenged (e.g. Goward and Prince 1995; Fernandez 2007) in scientific literature. The response of ecosystems to interannual variability in climate is a current and central topic in ecology since it reflects the vulnerability of ecosystem processes, and ultimately human well-being, to climate change (Rustad 2008; Nelson et al. 2013). Virtually every ecosystem may be impacted by climate change, however, the degree of the impact is likely to vary, as well as the magnitude of the ecosystem response. In this context, South American PAs provide a ‘laboratory’ to test expectations of climate change effects on ecosystem functioning at a landscape scale. Our analysis sought to answer the following questions: (1) How did the magnitude and seasonality of C gains change over South American ecosystems during the last three decades? (2) Were these changes concurrent with climate changes? (3) Which ecosystems were more sensitive to climatic fluctuations? We analyzed the temporal trends and spatial heterogeneity of both climatic variables and attributes of the seasonal dynamics of the NDVI on PAs of South America as a way to minimize the effects of land use/land cover changes over ecosystem functioning. Ecosystem functioning in South American Protected Areas a spatial resolution of c. 64 km2, a temporal resolution of 15 days and termed NDVI3g (third generation GIMMS NDVI from AVHRR sensors, downloaded from: ecocast.arc.nasa.gov). The NDVI3g dataset was assembled accounting for various deleterious effects, such as sensor degradation, calibration loss, orbital drift, volcanic eruptions, cloud cover and other effects not related to vegetation change and includes a quality assessment information value generated per pixel (Pinzon and Tucker 2014). Protected Areas boundaries were obtained from the most comprehensive global dataset on terrestrial protected areas as defined by IUCN: the World Database on Protected Areas (ProtectedPlanet.com, downloaded in February 2015). Protected Areas categorized as I or II by IUCN were selected as representative of natural ecosystems with limited human impact (Table 1). In order to check our assumption that using PAs of categories I and II minimized land use changes, we compared the cumulative human footprint inside and outside the PAs. To do so, we used globally standardized data on infrastructure, land cover and human access which summarize direct and indirect human pressures on the environment (Venter et al. 2016a,b). A map of biomes of South America (Olson et al. 2001) was used to derive vegetation units boundaries. Mean monthly precipitation and temperature gridded datasets at 0.5° spatial resolution were obtained from the Climatic Research Unit (CRU) of the University of East Anglia (http://www.cru.uea.ac.uk/data/). The CRU team Materials and Methods The analyses were based on NDVI data derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic & Atmospheric Agency (NOAA) satellites. The NDVI is calculated as the difference between the reflectance registered by the AVHRR sensor in the near-infrared (channel 2, 730– 1100 nm) and visible (channel 1, 580–680 nm) portion of the electromagnetic spectrum divided by the sum of the reflectance of both channels. The channel 2 is sensitive to atmospheric conditions since it encompass a broad wavelength interval and thus requires additional data or maximum value compositing for correcting aerosol, haze and clouds effects which can influence observed NDVI (Holben 1986). The GIMMS products (Tucker et al. 2005) are the only freely available for an extensive time period (1981–2015) and currently the most frequently used for evaluating patterns and trends around the world (Pettorelli 2013). While its reliability has been discussed by several authors (e.g. Baldi et al. 2008 and Alcaraz-Segura et al. 2010), good consistency between GIMMS and other NDVI products was also reported (Song et al. 2010; Beck et al. 2011; Zeng et al. 2013). We used a NDVI database spanning the period between July 1981 to December 2012 with Table 1. International Union for Conservation of Nature and Natural Resources (IUCN) protected area categories I and II (Dudley 2008). IUCN Category Description I a) Strict nature reserve Category Ia are strictly protected areas set aside to protect biodiversity and also possibly geological/geomorphological features, where human visitation, use and impacts are strictly controlled and limited to ensure protection of the conservation values. Such protected areas can serve as indispensable reference areas for scientific research and monitoring. Category Ib protected areas are usually large unmodified or slightly modified areas, retaining their natural character and influence, without permanent or significant human habitation, which are protected and managed so as to preserve their natural condition. Category II protected areas are large natural or near natural areas set aside to protect large-scale ecological processes, along with the complement of species and ecosystems characteristic of the area, which also provide a foundation for environmentally and culturally compatible spiritual, scientific, educational, recreational and visitor opportunities. I b) Wilderness area II) National park ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 3 Ecosystem functioning in South American Protected Areas constructed and updates this dataset from observations at meteorological stations across the world0 s land areas. Harris et al. (2014) compared it with other datasets and found a very good agreement for South America. Gridded data matched observations of temperatures much better than those of precipitation, and regional variation in performance indicated that topographically complex regions are the most difficult for these models (Behnke et al. 2016). CRU data were resampled through bilinear interpolation to NDVI3 g spatial resolution and aggregated to mean annual temperature (MAT) and total annual precipitation (TAP) over the period 1982–2012. We developed an NDVI time series based upon a monthly interval across the period 1982–2012 for every NDVI3g pixel located completely within a PA categorized as I or II by IUCN and not intersected by ecoregion limits. First, a filter based on NDVI3g quality flags was applied in order to exclude low quality and unreliable data, meaning that only pixels flagged as 1 o 2 (good value) were considered. Additionally, the bimonthly NDVI values were temporally aggregated to monthly maximum value composites in order to minimize problems not removed previously by the original processing or quality filtering (e.g. cloud contamination) as suggested by Holben (1986). Looking to further minimize noise and spurious values, non-vegetative pixels (NDVI ≤ 0.1) were also removed. In order to describe the patterns of ecosystem functioning, we derived for every year and pixel from our 1982–2012 monthly database four attributes that capture in a straightforward way the height and shape of the NDVI annual profile (Fig. 1): NDVI annual mean (NDVIm), an estimator of total radiation interception and ANPP, the annual maximum (Max) H. Dieguez & J.M. Paruelo and minimum (Min) NDVI values, related to the maximum and minimum photosynthetic capacity of the ecosystems and the intra-annual coefficient of variation of NDVI (CVt), a normalized index of vegetation seasonality (Paruelo and Lauenroth 1998; Pettorelli et al. 2005; Alcaraz et al. 2009; Volante et al. 2012). For each year, only pixels with at least 9 months of good quality values were considered and we excluded from analysis pixels with <7 years per decade of good quality data. Trends, indexed as the slope of the relationship between the variable and time, were assessed using the Theil-Sen estimator (Wilcox 2003), a method proposed by Theil (1950) and Sen (1968) that estimates the slope of a regression line by computing the slope for all pairs of data having distinct X values (corresponding to years in this study) and then computing the median of these slopes. This is a non-parametric test robust against seasonality, non-normality, heterocedasticity, missing values and inter-annual autocorrelation (Wilcox 2003). The association between trends in NDVI attributes and climatic variables were assessed using the Chi-square test of independence. To quantify the sensitivity of different ecosystems to variation in precipitation and temperature we estimated for each PA the slope of the linear relationship between NDVIm and TAP (Huxman et al. 2004; Ver on et al. 2005) and MAT through linear regression. Collinearity between TAP and MAT was assessed through the Pearson correlation coefficient (threshold > 0.7) and the variance inflation factor (threshold > 5) following Dormann et al. (2013). Slopes with a P < 0.05 were considered significant. Statistical analyses were performed in R (www.r-project.org). Results Figure 1. NDVI seasonal profile. NDVIm is the annual mean of NDVI and a surrogate of annual primary productivity. CVt is the intraannual coefficient of variation of NDVI and a normalized index of vegetation seasonality.Max and Min are annual NDVI maximum and minimum values respectively. NDVI, Normalized difference vegetation index. 4 Two-hundred-thirty-one of the 803 PAs under IUCN category I or II are large enough to contain at least one NDVI3g pixel completely within its boundaries. From this subset, we excluded from the analysis 31 PAs because of lack of good quality data. The remaining 201 PAs encompass 6286 NDVI3g pixels (402304 km2) and contain an average of 31 pixels (1984 km2) each one, ranging from 1 (64 km2) to 353 pixels (22592 km2). The PAs analyzed in this study belong to 61 ecoregions, 9 biomes and 13 countries, 35 (17%) correspond to IUCN category I and 166 (83%) to IUCN category II. They are distributed along a broad environmental gradient and a wide functional space (Fig. 2). Averages over the period 1982–2012 for MAT, TAP, NDVIm and CVt spanned from 4°C, 39 mm, 0.12 and 0.04–28°C, 5400 mm, 0.85 and 0.48 respectively. Only the most variable of the lower productivity ecosystems, corresponding to salt flats, the highest Andes and some of the driest deserts are ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Ecosystem functioning in South American Protected Areas Figure 2. (A) Geographical distribution of the protected areas included in this study (in black), and their distribution in the context of (B) annual mean of NDVI (NDVIm) and intra-annual coefficient of variation of NDVI (CVt), (C) Mean annual temperature (MAT) and mean annual precipitation (MAP) for the period 1982–2012 and (D) the mean of five variables measuring direct and indirect human pressures on the environment within a 0–10 scale (Venter et al. 2016a,b). In grey are displayed 10,000 points randomly distributed over South America. underrepresented in our dataset, because we purposely exclude non-vegetation areas and the fact that those systems have low coverage in the PAs network of South America (Juffe-Bignoli et al. 2014). Cumulative human footprint, indexed using land use, infrastructure and human access data, was considerably lower in protected than in unprotected areas (Fig. 2d). Both NDVIm and CVt significantly increased over the period 1982–2012 in South American protected ecosystems. When comparing 2008–2012 versus 1982–1986 averages, NDVIm and CVt increased 2.7% and 11,2%, respectively. Mean annual temperature showed a strong and significant positive trend over the period 1982–2012. The slope of the temporal trend of MAT for the 201 PAs jointly considered was 0.02°C.y-1. This value represent an average rise in MAT of 0.65°C during the past three decades. In contrast, TAP did not display a significant trend when considering together all PAs. At the individual PA level (Fig. 3 and Table 2), significant increases in NDVIm and MAT were dominant and the majority of the PAs showed non-significant changes in CVt and TAP. Upward trends in NDVIm were driven mostly by increases in Max, while increases in Min were less common. Max increases largely exceeded decreases, however, Min increases were scarce and Min decreases widespread. Chisquare test showed that trends in NDVIm were associated with trends in TAP (P < 0.0001) and MAT (P < 0.05) while CVt trends were independent of the trends observed in climatic variables. Biomes differed in the magnitude and direction of the changes (Fig. 4). Significant increases in NDVIm and CVt were more common than decreases, however non-significant changes were dominant. The proportion of surface under protection which showed increases in NDVIm ranged between 30% and 41% in tropical and subtropical biomes, in contrast, decreases in NDVIm were found in 17% of its grasslands, savannas and shrublands and in <5% of its forests. A similar pattern was found for CVt in those biomes. The opposite occurred in flooded grasslands and savannas, where significant decreases in NDVIm exceeded the positives changes (26% vs. 9% of the total protected surface) and CVt significantly increased in almost 60% of its area. Temperate grasslands, savannas and shrublands exhibited significant changes in NDVIm in only 8%, and in CVt in 15% of its protected surface. More than 80% of the area located in PAs in mangroves, flooded grasslands and savannas, tropical and subtropical grasslands, savannas, shrublands and moist broadleaf forests showed significant increases in MAT, while in deserts and xeric shrublands and tropical and subtropical dry broadleaf forests the increase was significant in near 40% of the area. In contrast, montane grasslands and shrublands showed a significant decrease of MAT in 42% of its area. Regarding TAP, it significantly increased in 42% of temperate broadleaf and mixed forests and 13% of temperate grasslands, savannas and shrublands and decreased in 50% and 16% of tropical and subtropical dry broadleaf forests and grasslands, savannas and shrublands respectively, while more than 90% of the area located in the other biomes showed non-significant trends. Interannual fluctuation in NDVIm was significantly related to interannual fluctuations in precipitation, mean ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 5 Ecosystem functioning in South American Protected Areas H. Dieguez & J.M. Paruelo Figure 3. Spatial heterogeneity of temporal trends in NDVIm, CVt, Min, Max, TAP and MAT, and sensitivity of NDVIm to TAP and MAT fluctuations. Trends were indexed as the slope of the relation of the variable and time during the period 1982–2012. Sensitivities were indexed as the slope of the relationship of NDVIm and annual MAT or TAP across years for the period 1982–2012. Dots represent 201 protected areas corresponding to IUCN categories I or II and show significant positive (blue), non-significant (grey) or significant negative (red) values. NDVIm, NDVI annual mean; CVt, Intra-annual coefficient of variation of the NDVI; TAP, Total annual precipitation; MAT, Mean annual temperature; Ps, Sensitivity of NDVIm to TAP interannual fluctuation; Ts, Sensitivity of NDVIm to MAT interannual fluctuation; TSMBF, Tropical & Subtropical Moist Broadleaf Forests; TSDBF, Tropical & Subtropical Dry Broadleaf Forests; TBMF, Temperate Broadleaf & Mixed Forests; TSGSS, Tropical & Subtropical Grasslands, Savannas & Shrublands; TGSS, Temperate Grasslands, Savannas & Shrublands; FGS, Flooded Grasslands & Savannas; MGS, Montane Grasslands & Shrublands; DXS, Deserts & Xeric Shrublands; M, Mangroves. 6 ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Ecosystem functioning in South American Protected Areas Table 2. Count of protected areas (n = 201) showing significant negative (), non-significant (ns) and significant positive (+) trends in NDVI mean (NDVIm), Intra-annual coefficient of the variation of the NDVI (CVt), Total annual precipitation (TAP) and Mean annual temperature (MAT) across the period 1982–2012. Trends, indexed as the slope of the relationship between the variable and time, were assessed using the Theil-Sen estimator. Slopes with a P-value < 0.05 were considered significant. NDVIm CVt TAP MAT ns + Ns + ns + 3 7 4 6 1 4 5 ns 4 97 19 3 105 12 13 51 56 CVt + TAP ns + 4 128 17 13 50 86 3 39 6 6 13 29 ns + 1 18 60 93 7 16 49 22 60 11 5 12 54 4 4 6 temperature or both climatic variables in 79 PAs (39%). Collinearity between MAT and TAP was below the recommended threshold in all cases. Significant relationships between NDVIm and precipitation across years were found in 31 PAs, being 22 direct and 9 inverse relationships. Significant relationships between NDVIm and temperature across years were found in 58 PAs, being 56 direct and 2 inverse relationships. Ten PAs showed significant relationships between NDVIm, precipitation and temperature across years. In general, arid and semiarid sites (e.g. PAs located in Argentine Patagonia and Brazilian Caatinga) responded positively to increases in precipitation and negatively to increases in temperature, while humid forests (e.g. temperate forests of Patagonia, and Amazonian tropical and subtropical forests) responded in the opposite way (Fig. 3). Detailed results of individual PAs are provided in the supplementary material. Discussion In this article we analyzed c. 402.300 km2 of protected ecosystems in South America, an area equivalent to Paraguay, where some of the driest (Salar de Huasco, Chile) and wettest (Utria, Colombia) sites of the world are represented along with deserts, grasslands, savannas, dry forest and tropical rainforests. Our analysis enabled us to characterize changes on ecosystem functioning at a regional scale and provide a preliminary basis for predicting which ecosystems will change its productivity more rapidly and strongly in response to climate change, that is, those with the highest sensitivity and the largest changes in climatic variables. Increased productivity and higher seasonality, frequently climate driven, is the most common signal across the least modified areas of South American biomes. However, those sites where climate is becoming more arid (upward temperature and downward precipitation trends respectively) showed significant reductions in productivity. Warming and increased variability in precipitation is predicted by climate models (IPCC 2013), but changes in ecosystem functioning depend on the interactions among other factors such as nutrient availability, radiation and changes in plant community composition and structure. These interactions represent one of the largest uncertainties in projections of future ecosystem functioning change. We found evidences for an increase in C gains (as indexed by NDVIm) during the last three decades over the majority of South American PAs, but concentrated on those located in the tropics and subtropics. An overall increase in C gains during the last decades in South America (assessed using AVHRR-NOAA datasets) was previously reported (Nemani et al. 2003; Paruelo et al. 2004; Baldi et al. 2008; Beck et al. 2011). Long term field monitoring plots also showed a similar pattern (Phillips et al. 1998). Nemani et al. (2003) related C gains increases with the release of climatic constraints, such as declining cloud cover in the Amazon. Mueller et al. (2014) suggested that land use practices could be behind NDVI trends and found that positive trends were associated with intensive land use. In relation with land use, more detailed analysis showed that land clearing for agriculture and overgrazing could be responsive of a reduction in C gains across years while afforestation generated NDVI upward trends (Paruelo et al. 2004; Baldi et al. 2008; Eastman et al. 2013; Vassallo et al. 2013; Texeira et al. 2015). Carbon gains increases should be considered with caution regarding C balances. Net C balance outcomes are less clear since respiration frequently shows stronger sensitivity to warming (Heimann and Reichstein 2008) and therefore C losses can potentially offset C gains (Crowther et al. 2016). On the other hand, ecosystem services provision and biodiversity can be compromised by the decrease in C gains observed in some arid and semiarid sites (Paruelo et al. 2016). Other causes may be invoked for changes in C gains (e.g. CO2 fertilization, biological invasions, increased N deposition) and deserve further investigation (Zhu et al. 2016). Focusing on protected areas, and hence minimizing land use effects, we found correlative evidence of a positive relationship between C gains trends and temperature and precipitation trends. However, although in different frequency, all possible combination between trends in NDVIm and trends in TAP or MAT were found, supporting the idea that the response to climatic variables varies among ecosystems. While the release of climatic constraints can be true for tropical and subtropical ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 7 Ecosystem functioning in South American Protected Areas H. Dieguez & J.M. Paruelo Figure 4. Proportion of area (%) showing significant positive (blue), non-significant (white) and significant negative (red) trends per biome. Trends were indexed as the slope of the relation of the variable and time during the period 1982–2012. Only pixels not intersected by biome boundaries were considered. NDVIm, NDVI annual mean; CVt, Intra-annual coefficient of variation of the NDVI. TAP, Total annual precipitation; MAT, Mean annual temperature; TSMBF, Tropical & Subtropical Moist Broadleaf Forests; TSDBF, Tropical & Subtropical Dry Broadleaf Forests; TBMF, Temperate Broadleaf & Mixed Forests; TSGSS, Tropical & Subtropical Grasslands Savannas & Shrublands; TGSS, Temperate Grasslands, Savannas & Shrublands; FGS, Flooded Grasslands & Savannas; MGS, Montane Grasslands & Shrublands; DXS, Deserts & Xeric Shrublands; M, Mangroves. ecosystems, increases in temperature and decreasing precipitation can be limiting productivity in some temperate and semiarid sites. Upward trends in NDVI intraannual coefficient of variation, which means a higher variation of the primary productivity through the year, largely exceeded decreases, however non-significant trends were dominant. Seasonality increases when minimum NDVI becomes lower and/or maximum NDVI becomes higher. As for NDVIm, increases in CVt were driven mostly by increases in Max, which is related to an increase in productivity during the growing season, with the dormant season not being modified. We were unable to find a climatic association with CVt shifts, probably because of the coarse temporal scale of our analysis, however we found a significant positive association between Min and TAP changes and Max and MAT 8 changes. It has been suggested that vegetation seasonality is profoundly impacted by land use change, with significant increases after land clearing for agriculture (Guerschman et al. 2003; Volante et al. 2012), but other drivers not related to land conversion were also identified (Eastman et al. 2013). Our results, gathered on more natural, less modified ecosystems support the idea that other factors than direct human interventions may be operating on generating changes in seasonality. Seasonality changes attributed to land transformation (e.g. Baldi et al. 2008) can be confounded or overestimated if concurrent seasonality changes in natural systems are not taken into account. Furthermore, as regarding C gains decreases, seasonality increases can compromise ecosystem services provision and biodiversity (Paruelo et al. 2016). ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Interannual variation in climatic variables significantly accounted for interannual variation in NDVIm in only 40% of the sites. This can be the outcome of several biogeochemical and vegetation constraints on the response to climatic fluctuations (Fernandez 2007). These results are similar to the findings of other analyses based on remote sensing (Schultz and Halpert 1993; Ichii et al. 2002) and field studies performed in the northern hemisphere (e.g. Sala et al. 2012; Mowll et al. 2015). Besides intrinsic differences in sensitivity to changes in climate, other factors related to the temporal scale and not considered in this analysis (e.g. lags that result from legacies of previous years, seasonality, timing and intensity of extreme events) can explain the lack of response of some ecosystems to climatic fluctuation. Furthermore, aspects related to non-linearity or thresholds in the response of productivity to climatic variables may not be captured in our analysis. However, our results regarding C gains sensitivity to climatic fluctuations is coherent with ecosystem-level field experimental evidence and landscape-level observations. Productivity and precipitation changes were found to be positively linked in arid or semiarid lands (Zhao and Running 2010; Wu et al. 2011; Pettorelli et al. 2012) being drier ecosystems more sensitive to increased precipitation (Huxman et al. 2004). The temporal coupling of vegetation activity and water availability was previously reported over the Caatinga region of northeast Brazil by Barbosa et al. (2006) and over Patagonia by Jobbagy et al. (2002). The reduction in C gains related to increased precipitation observed in the forests over the Southern Andes (Fig. 3) can be the result of an extended snow cover during those years with precipitation above the mean. This negative correlation was observed in northern high-latitudes and mountainous regions of the world (Los et al. 2001). Warming increased productivity in cold or not water-limited ecosystems (Rustad et al. 2001; Goetz et al. 2005) as observed in high latitudes and altitudes, and in tropical forests in this study. A reduction in C gains can be expected after heat waves or combined with drought (Ciais et al. 2005; Wu et al. 2011). But despite the significant rise in mean temperature observed during the last decades over the Amazon (Fig. 3), our results showed upward trends in tropical forests C gains. Furthermore, we found a positive response of C gains to increases in temperature which mean that the thermal limit of tropical forests would not be reached yet, as it was suggested for other tropical forests (Clark et al. 2003). We found a large amount of C gains interannual variability not explained by variability in climatic variables (not shown). Other interacting factors such as nutrient availability, radiation (Nemani et al. 2003; Seddon et al. 2016) and changes in plant community composition and structure (Wilcox et al. 2016) Ecosystem functioning in South American Protected Areas should be taken into account and deserves further investigation. Conclusion We found that warming, increased productivity and higher seasonality are the most common signals of environmental change across South American biomes. Furthermore, we provided empirical evidence of a positive relationship between changes in C gains and changes in climate (temperature and precipitation) for the least modified ecosystems in South America. Our results provide a preliminary basis for predicting which ecosystems will change its productivity more rapidly and strongly in response to climate change, that is, those with the highest sensitivity and the largest changes in climatic variables (Fig. 3). Interestingly, we found that functional consequences of climate change can be similar to those expected from land use/cover changes (e.g. land clearing for agriculture). We advocate the use of PAs as a reference situation (Garbulsky and Paruelo 2004) to track the effects of climate change (Pettorelli et al. 2012). Atmospheric deposition networks, maps of invasive species and a better understanding of the mechanisms which modulate the response of different ecosystems to increased atmospheric CO2, will help to comprehend the effects of Global Change on ecosystem functioning. Such studies need to be complemented with more detailed analyses, based on experiments and modeling studies. Natural experiments, where some factors are fixed across environmental gradients (e.g. land use in protected areas or vegetation type in widely distributed forest plantations), represent an attractive approach to gain insights into short and long-term effects, and also spatial heterogeneity, of environmental changes on ecosystems. Such natural experiments can be used to disentangle the relative importance of factors such as land use, climatic and biogeochemical changes driving ecosystem functioning to improve forecast of vegetation change, and hence, ecosystem services provision. Acknowledgements We thank Sebastian Aguiar and Marcos Texeira for assistance during data gathering and analysis. Sebastian Aguiar, Gervasio Pi~ neiro, Santiago Ver on and anonymous referees generously made valuable comments and suggestions that improved our paper. This work was carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI) CRN3095 which is supported by the US National Science Foundation (Grant GEO-1128040). CONICET and UBA provided additional funds. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 9 Ecosystem functioning in South American Protected Areas References Alcaraz, D., J. Cabello, J. Paruelo, and M. Delibes. 2009. Use of descriptors of ecosystem functioning for monitoring a national park network: a remote sensing approach. Environ. Manage. 43, 38–48. Alcaraz-Segura, D., J. Cabello, J. M. Paruelo, and M. Delibes. 2008. Trends in the surface vegetation dynamics of the national parks of Spain as observed by satellite sensors. Appl. Veg. Sci. 11, 431–440. Alcaraz-Segura, D., E. Liras, S. Tabik, J. Paruelo, and J. Cabello. 2010. Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II. Sensors 10, 1291–1314. Alcaraz-Segura, D., J.M. Paruelo, H.E. Epstein, and J. Cabello. 2013. Environmental and human controls of ecosystem functional diversity in temperate South America. Remote Sens., 5, 127–154. Baldi, G., M. D. Nosetto, R. Arag on, F. Aversa, J. M. Paruelo, and E. G. Jobbagy. 2008. Long-term satellite NDVI data sets: evaluating their ability to detect ecosystem functional changes in South America. Sensors 8, 5397–5425. Barbosa, H. A., A. R. Huete, and W. E. Baethgen. 2006. A 20year study of NDVI variability over the northeast region of Brazil. J. Arid Environ. 67, 288–307. Barral, M. P., and N. Maceira. 2012. Land-use planning based on ecosystem service assessment: a case study in the Southeast Pampas of Argentina. Agric. Ecosyst. Environ. 154, 34–43. Beck, H. E., T. R. McVicar, A. I. J. M. van Dijk, J. Schellekens, R. A. M. de Jeu, and L. A. Bruijnzeel. 2011. Global evaluation of four AVHRR–NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens. Environ. 115, 2547–2563. Behnke, R., S. Vavrus, A. Allstadt, T. Albright, W. E. Thogmartin, and V. C. Radeloff. 2016. Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol. Appl. 26, 1338–1351. Boisvenue, C., and S. W. Running. 2006. Impacts of climate change on natural forest productivity - evidence since the middle of the 20th century. Glob. Change Biol. 12, 862–882. Cabello, J., N. Fernandez, D. Alcaraz-Segura, C. Oyonarte, G. Pi~ neiro, A. Altesor, et al. 2012. The ecosystem functioning dimension in conservation: insights from remote sensing. Biodivers. Conserv. 21, 3287–3305. Carre~ no, L., F. C. Frank, and E. F. Viglizzo. 2012. Tradeoffs between economic and ecosystem services in Argentina during 50 years of land-use change. Agric. Ecosyst. Environ. 154, 68–77. Ciais, P., M. Reichstein, N. Viovy, A. Granier, J. Ogee, V. Allard, et al. 2005. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2003, 529–533. 10 H. Dieguez & J.M. Paruelo Clark, D. A., S. C. Piper, C. D. Keeling, and D. B. Clark. 2003. Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984-2000. Proc. Natl Acad. Sci. USA 100, 5852–5857. Cook, J., N. Oreskes, P. T. Doran, W. R. L. Anderegg, B. Verheggen, E. W. Maibach, et al. 2016. Consensus on consensus: a synthesis of consensus estimates on humancaused global warming. Environ. Res. Lett. 11, 048002. Costanza, R. 1992. Towards an operational definition of ecosystem health. In R. Costanza, B. G. Norton, B. D. Haskell, eds. Ecosystem health: new goals for environmental management.Island Press, Washington, D.C. Crowther, T. W., K. E. O. Todd-Brown, C. W. Rowe, W. R. Wieder, J. C. Carey, M. B. Machmuller, et al. 2016. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108. Cumming, G. S., C. R. Allen, N. C. Ban, D. Biggs, H. C. Biggs, D. H. M. Cumming, et al. 2015. Understanding protected area resilience: a multi-scale, social-ecological approach. Ecol. Appl. 25, 299–319. DeFries, R., J. Foley, and G. P. Asner. 2004. Land use choices: balancing human needs and ecosystem function. Front. Ecol. Environ. 2, 249–257. Di Bella, C., J. M. Paruelo, J. E. Becerra, C. Bacour, and F. Baret. 2004. Effect of senescent leaves on NDVI-based estimates of fAPAR: experimental and modelling evidences. Int. J. Remote Sens. 25, 5415–5427. Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carre, et al. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. Dudley, N. (ed.) (2008) Guidelines for Applying Protected Area Management Categories. IUCN, Gland, Switzerland. x + 86pp. ISBN: 978-2-8317-1086-0. Dudley, N., K. MacKinnon, and S. Stolton. 2014. The role of protected areas in supplying ten critical ecosystem services in drylands: a review. Biodiversity 15, 178–184. Eastman, J., F. Sangermano, E. Machado, J. Rogan, and A. Anyamba. 2013. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sens. 5, 4799–4818. Fernandez, R. 2007. On the frequent lack of response of plants to rainfall events in arid areas. J. Arid Environ. 68, 688–691. Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, et al. 2005. Global Consequences of land use. Science 309, 570–574. Garbulsky, M. F., and J. M. Paruelo. 2004. Remote sensing of protected areas to derive baseline vegetation functioning characteristics. J. Veg. Sci. 15, 711–720. Goetz, S. J., A. G. Bunn, G. J. Fiske, and R. A. Houghton. 2005. Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proc. Natl Acad. Sci. USA 102, 13521–13525. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Goward, S. N., and S. D. Prince. 1995. Transient effects of climate on vegetation dynamics: satellite observations. J. Biogeogr. (United Kingdom) 22, 549–563. Guerschman, J. P., J. M. Paruelo, and I. C. Burke. 2003. Land use impacts on the normalized difference vegetation index in temperate Argentina. Ecol. Appl. 13, 616–628. Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, et al. 2013. High-resolution global maps of 21st-century forest cover change. Science (New York, N.Y.) 342, 850–853. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister. 2014. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642. Heimann, M., and M. Reichstein. 2008. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292. Hicke, J. A., G. P. Asner, J. T. Randerson, C. Tucker, S. Los, R. Birdsey, et al. 2002. Trends in North American net primary productivity derived from satellite observations, 1982-1998. Global Biogeochem. Cycles 16, 1–15. Hilker, T., A. I. Lyapustin, C. J. Tucker, F. G. Hall, R. B. Myneni, Y. Wang, et al. 2014. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046. Hoekstra, J. M., T. M. Boucher, T. H. Ricketts, and C. Roberts. 2005. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. 8, 23–29. Holben, B. 1986. Characteristics of maximum value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434. Huntington, T. 2006. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319, 83–95. Huxman, T. E., M. D. Smith, P. A. Fay, A. K. Knapp, M. R. Shaw, M. E. Loik, et al. 2004. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654. Ichii, K., A. Kawabata, and Y. Yamaguchi. 2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. Int. J. Remote Sens. 23, 3873–3878. IPCC. 2007. Climate change 2007. Synthesis report, summary for policymakers. Avaliable at: http://www.ipcc.ch/ipcc reports/ar4-syr.htm. IPCC. 2013. Summary for policymakers. in T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, P. M. Midgley, eds. Climate Change 2013: the physical science basis. contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY. Jobbagy, E. G., O. E. Sala, and J. M. Paruelo. 2002. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83, 307–319. Juffe-Bignoli, D., N. D. Burgess, H. Bingham, E. M. S. Belle, M. G. de Lima, M. Deguignet, et al. 2014. Protected planet report 2014. UNEP-WCMC, Cambridge, UK. Ecosystem functioning in South American Protected Areas Kerr, J. T., and M. Ostrovsky. 2003. From space to species: ecological applications for remote sensing. Trends Ecol. Evol. 18, 299–305. Lauenroth, W. K., and O. E. Sala. 1992. Long-term forage production of north american shortgrass steppe. Ecol. Appl. 2, 397–403. Leisher, C., J. Touval, S. Hess, T. Boucher, and L. Reymondin. 2013. Land and forest degradation inside protected areas in Latin America. Diversity 5, 779–795. Lieth, H. 1973. Primary production: terrestrial ecosystems. Human Ecol. 1, 303–332. Lopoukhine, N., N. Crawhall, N. Dudley, P. Figgis, C. Karibuhoye, D. Laffoley, et al. 2012. Protected areas: providing natural solutions to 21st Century challenges. S.A.P.I.EN.S. 5.2, 116–131. Los, S. O., G. J. Collatz, L. Bounoua, P. J. Sellers, and C. J. Tucker. 2001. global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation. J. Clim. 14, 1535–1549. Mann, M. E., Z. Zhang, M. K. Hughes, R. S. Bradley, S. K. Miller, S. Rutherford, et al. 2008. Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proc. Natl Acad. Sci. 105, 13252–13257. McNaughton, S. J., M. Oesterheld, D. A. Frank, and K. J. Williams. 1989. Ecosystem-level patterns of primary productivity and herbivory in terrestrial habitats. Nature 341, 142–144. Monteith, J. L. 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9, 747–766. Mowll, W., D. M. Blumenthal, K. Cherwin, A. Smith, A. J. Symstad, L. T. Vermeire, et al. 2015. Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming. Oecologia 177, 959–969. Mueller, T., G. Dressler, C. Tucker, J. Pinzon, P. Leimgruber, R. Dubayah, G. et al. 2014. Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity. Remote Sens., 6, 5717–5731. Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. B. da Fonseca, and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403, 853–858. Nelson, E. J., P. Kareiva, M. Ruckelshaus, K. Arkema, G. Geller, E. Girvetz, et al. 2013. Climate change’s impact on key ecosystem services and the human well-being they support in the US. Front. Ecol. Environ. 11, 483–893. Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, et al. 2003. Climate-Deriven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563. Odum, E. P. 1969. The strategy of ecosystem development. Science 164, 262–270. Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, et al. 2001. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 11 Ecosystem functioning in South American Protected Areas Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933. Paruelo, J. M., and W. K. Lauenroth. 1998. Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands. J. Biogeogr. 25, 721– 733. Paruelo, J. M., H. E. Epstein, W. K. Lauenroth, and I. C. Burke. 1997. ANPP estimates from NDVI for the central grassland region of the US. Ecology 78, 953–958. Paruelo, J. M., M. F. Garbulsky, J. P. Guerschman, and E. G. Jobbagy. 2004. Two decades of normalized difference vegetation index changes in South America: identifying the imprint of global change. Int. J. Remote Sens. 25, 2793–2806. Paruelo, J. M., M. Texeira, L. Staiano, M. Mastrangelo, L. Amdan, and F. Gallego. 2016. An integrative index of ecosystem services provision based on remotely sensed data. Ecol. Ind. 71, 145–154. Pettorelli, N. 2013. The normalized difference vegetation index. Oxford University Press, Oxford, UK. Pettorelli, N., J. O. Vik, A. Mysterud, J. M. Gaillard, C. J. Tucker, and N. C. Stenseth. 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510. Pettorelli, N., A. L. M. Chauvenet, J. P. Duffy, W. A. Cornforth, A. Meillere, and J. E. M. Baillie. 2012. Tracking the effect of climate change on ecosystem functioning using protected areas: Africa as a case study. Ecol. Ind. 20, 269– 276. Phillips, O. L., Y. Malhi, N. Higuchi, W. F. Laurance, P. V. Nunez, R. M. Vasquez, et al. 1998. Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282, 411–439. Pi~ neiro, G., M. Oesterheld, and J. M. Paruelo. 2006. Seasonal variation in aboveground production and radiation use efficiency of temperate rangelands estimated through remote sensing. Ecosystems 9, 357–373. Pinzon, J., and C. Tucker. 2014. A Non-Stationary 1981– 2012 AVHRR NDVI3 g Time Series. Remote Sens. 6, 6929–6960. Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, et al. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem. Cycles 7, 811–841. Ruimy, A., B. Saugier, and G. Dedieu. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. 99, 5263–5283. Rustad, L. 2008. The response of terrestrial ecosystems to global climate change: towards an integrated approach. Sci. Total Environ. 404, 222–235. Rustad, L., J. Campbell, G. Marion, R. Norby, M. Mitchell, A. Hartley, et al. 2001. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 126, 543–562. 12 H. Dieguez & J.M. Paruelo Sala, O. E., F. S. III Chapin, J. J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, et al. 2000. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774. Sala, O. E., L. A. Gherardi, L. Reichmann, E. Jobbagy, and D. Peters. 2012. Legacies of precipitation fluctuations on primary production: theory and data synthesis. Philos. Trans. Royal Soc. Lond. B Biol. Sci. 367, 3135–3144. Schlesinger, W. H. 1997. An analysis of global change. 2nd ed.Biogeochemistry. Academic Press, San Diego. Schultz, P. A., and M. S. Halpert. 1993. Global correlation of temperature, NDVI and precipitation. Adv. Space Res. 13, 277–280. Seddon, A. W. R., M. Macias-Fauria, P. R. Long, D. Benz, and K. J. Willis. 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232. Sellers, P. J., S. O. Los, C. J. Tucker, C. O. Justice, D. A. Dazlich, G. J. Collatz, et al. 1996. A revised land surface parameterization (Sib2) for atmospheric Gcms. Part II: the generation of global fields of terrestrial biophysical parameters from satellite data. J. Clim. 9, 706–737. Sen, P. K. 1968. estimates of the regression coefficient based on kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389. Skansi, M., M. Brunet, J. Sigr o, E. Aguilar, J. Arevalo Groening, O. Bentancur, et al. 2013. Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Global Planet. Change 100, 295–307. Song, Y., M. Ma, and F. Veroustraete. 2010. Comparison and conversion of AVHRR GIMMS and SPOT VEGETATION NDVI data in China. Int. J. Remote Sens. 31, 2377–2392. Spiecker, H. 1999. Overview of recent growth trends in European forests. Water Air Soil Pollut. 116, 33–46. Texeira, M., M. Oyarzabal, G. Pineiro, S. Baeza, and J. M. Paruelo. 2015. Land cover and precipitation controls over long-term trends in carbon gains in the grassland biome of South America. Ecosphere 6:art196. Theil, H. 1950. A rank-invariant method of linear and polynomial regression analysis. Indagationes Mathematicae 12, 85–91. Tucker, C. J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, et al. 2005. An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI Data. Int. J. Remote Sens. 26, 4485–4498. UN. 2011. The Millennium Development Goals Report 2011: Statistical Annex. (United Nations, Ed.). New York, USA. Vassallo, M. M., H. D. Dieguez, M. F. Garbulsky, E. G. Jobbagy, and J. M. Paruelo. 2013. Grassland afforestation impact on primary productivity: a remote sensing approach. Appl. Veg. Sci. 16, 390–403. Venter, O., E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, et al. 2016a. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. H. Dieguez & J.M. Paruelo Venter, O., E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, et al. 2016b. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3: 160067. Ver on, S. R., M. Oesterheld, and J. M. Paruelo. 2005. Production as a function of resource availability : slopes and efficiencies are different. J. Veg. Sci. 16, 351–354. Vitousek, P. M. 1994. Beyond global warming: ecology and global change. Ecology 75, 1861–1876. Vitousek, P. M., J. D. Aber, R. W. Howarth, G. E. Likens, P. A. Matson, D. W. Schindler, et al. 1997. Human alteration of the global nitrogen cycle: sources and consequences. Ecol. Appl. 7, 737–750. Volante, J. N., D. Alcaraz-Segura, M. J. Mosciaro, E. F. Viglizzo, and J. M. Paruelo. 2012. Ecosystem functional changes associated with land clearing in NW Argentina. Agric. Ecosyst. Environ. 154, 12–22. Watson, J. E. M., N. Dudley, D. B. Segan, and M. Hockings. 2014. The performance and potential of protected areas. Nature 515, 67–73. Wilcox, R. R. 2003. Applying contemporary statistical techniques. Academic Press, Amsterdam. Wilcox, K. R., J. M. Blair, M. D. Smith, and A. K. Knapp. 2016. Does ecosystem sensitivity to precipitation at the site-level conform to regional-scale predictions? Ecology 97, 561–568. Wu, Z., P. Dijkstra, G. W. Koch, J. Pe~ nuelas, and B. A. Hungate. 2011. Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis Ecosystem functioning in South American Protected Areas of experimental manipulation. Glob. Change Biol. 17, 927– 942. Xiao, J., and A. Moody. 2004. Photosynthetic activity of US biomes: responses to the spatial variability and seasonality of precipitation and temperature. Glob. Change Biol. 10, 437–451. Zeng, F.-W., G. Collatz, J. Pinzon, and A. Ivanoff. 2013. Evaluating and quantifying the climate-driven interannual variability in global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3 g) at global scales. Remote Sens. 5, 3918–3950. Zhao, M., and S. W. Running. 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943. Zhu, Z., S. Piao, R. B. Myneni, M. Huang, Z. Zeng, J. G. Canadell, et al. 2016. Greening of the earth and its drivers. Nat. Clim. Chang. 6, 791–795. Supporting Information Additional supporting information may be found online in the supporting information tab for this article. Figure. S1. Location of the 201 protected areas analyzed in this study. Biomes boundaries by Olson (2001). Table S1. Average values and trends in NDVIm, CVt, TAP and MAT, and climate sensitivity of NDVIm to TAP and MAT fluctuations for the 201 protected areas analyzed in this study. ª 2017 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. 13